The Growth of Computer Science Education in Alberta: An Analysis of High School Course Completion Trends
Bibliographic record
Abstract
Computer Science (CS) education is an emergent growth area in schools worldwide. This paper explores how CS education has evolved at the high school level (grades 10–12) in the Canadian province of Alberta over the past decade after a reorganization and curriculum redesign of its Computing Science Education (CSE) program. In partnership with Alberta Education, a complete list of course records was obtained for high school students who had taken CSE course credits between 2009 and 2019. These course completions were analyzed for overall growth trends and then further examined with respect to course level, urbanicity, and gender. We found that growth in course credit completion has been consistent over the 10-year study period (annual average growth rate of 33.5%). Advanced course credits have grown faster than introductory course credits, urban areas have grown faster than rural areas, and gender growth rates have been similar for males and females. Understanding the growth rates of CSE course enrollments at the high school level will contribute to identifying some of the challenges encountered during the implementation of the CSE program of studies in Alberta. Keywords: High School Computer Science Education; Gender Participation. L'enseignement de l'informatique est un domaine de croissance émergent dans les écoles du monde entier. Cet article explore l'évolution de l'enseignement de l'informatique au niveau secondaire (10e à 12e année) dans la province canadienne de l'Alberta au cours de la dernière décennie, après une réorganisation et une refonte du programme d'études de l'informatique. En partenariat avec le ministère de l'éducation de l'Alberta, on a obtenu une liste complète des dossiers de cours pour les élèves du secondaire qui ont reçu des crédits pour des cours d’informatique entre 2009 et 2019. Ces cours ont été analysés pour déterminer les tendances générales de croissance, puis examinés plus en détail en fonction du niveau de cours, de l'urbanité et du sexe. Nous avons constaté que la croissance de l'obtention de crédits de cours a été constante au cours de la période d'étude de 10 ans (taux de croissance annuel moyen de 33,5 %). Les crédits de cours avancés ont augmenté plus rapidement que les crédits de cours d'introduction, les zones urbaines ont augmenté plus rapidement que les zones rurales, et les taux de croissance ont été similaires pour les hommes et les femmes. La compréhension des taux de croissance des inscriptions aux cours d'informatique au niveau secondaire contribuera à identifier certains des défis rencontrés lors de la mise en œuvre du programme d'études en informatique en Alberta. Mots clés : Enseignement de l'informatique au niveau secondaire ; participation des hommes et des femmes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".